arXiv subjects

A. Ryzhov

Publications and source records attributed to A. Ryzhov.

6 recordsLinked to original sources

arXiv2024

The thermoelectric conversion efficiency problem: Insights from the electron gas thermodynamics close to a phase transition

The bottleneck in modern thermoelectric power generation and cooling is the low energy conversion efficiency of thermoelectric materials. The detrimental effects of lattice phonons on performance can be mitigated, but achieving a high thermoelectric power factor remains a major problem because the Seebeck coefficient and electrical conductivity cannot be jointly increased. The conducting electron gas in thermoelectric materials is the actual working fluid that performs the energy conversion, so its properties determine the maximum efficiency that can theoretically be achieved. By relating the thermoelastic properties of the electronic working fluid to its transport properties (considering noninteracting electron systems), we show why the performance of conventional semiconductor materials is doomed to remain low. Analyzing the temperature dependence of the power factor theoretically in 2D systems and experimentally in a thin film, we find that in the fluctuation regimes of an electronic phase transition, the thermoelectric power factor can significantly increase owing to the increased compressibility of the electron gas. We also calculate the ideal thermoelectric conversion efficiency in noninteracting electron systems across a wide temperature range neglecting phonon effects and dissipative coupling to the heat source and sink. Our results show that driving the electronic system to the vicinity of a phase transition can indeed be an innovative route to strong efficiency enhancement, but at the cost of an extremely narrow temperature range for the use of such materials, which in turn precludes potential development for the desired wide range of thermoelectric energy conversion applications.

cond-mat.mtrl-sci↗

arXiv2022

The application of encoder-decoder neural networks in high accuracy and efficiency slit-scan emittance measurements

A superconducting radio-frequency (SRF) photo injector is in operation at the electron linac for beams with high brilliance and low emittance (ELBE) radiation center and generates continuous wave (CW) electron beams with high average current and high brightness for user operation since 2018. The speed of emittance measurement at the SRF gun beamline can be increased by improving the slit-scan system, thus the measurement time for one phase space mapping can be shortened from about 15 minutes to 90 seconds. A parallel algorithm and machine learning have been used to reduce the beamlet image noise. In order to estimate the uncertainty in the calculation of normalized emittance, we analyze the main error contributions such as slit position uncertainty, image noise, space charge effects and energy measurement inaccuracy.

physics.acc-ph↗

arXiv2021

QGOpt: Riemannian optimization for quantum technologies

Many theoretical problems in quantum technology can be formulated and addressed as constrained optimization problems. The most common quantum mechanical constraints such as, e.g., orthogonality of isometric and unitary matrices, CPTP property of quantum channels, and conditions on density matrices, can be seen as quotient or embedded Riemannian manifolds. This allows to use Riemannian optimization techniques for solving quantum-mechanical constrained optimization problems. In the present work, we introduce QGOpt, the library for constrained optimization in quantum technology. QGOpt relies on the underlying Riemannian structure of quantum-mechanical constraints and permits application of standard gradient based optimization methods while preserving quantum mechanical constraints. Moreover, QGOpt is written on top of TensorFlow, which enables automatic differentiation to calculate necessary gradients for optimization. We show two application examples: quantum gate decomposition and quantum tomography.

quant-ph↗

arXiv2020

Non-rotational mechanism of polarization in alcohols

Chemical polarity governs various mechanical, chemical and thermodynamic properties of dielectrics. Polar liquids have been amply studied, yet the basic mechanisms underpinning their dielectric properties remain not fully understood, as standard models following Debye's phenomenological approach do not account for quantum effects and cannot aptly reproduce the full dc-up-to-THz spectral range. Here, using the illustrative case of monohydric alcohols, we show that deep tunneling and the consequent intermolecular separation of excess protons and "proton-holes" in the polar liquids govern their static and dynamic dielectric properties on the same footing. We performed systematic ultrabroadband (0-10 THz) spectroscopy experiments with monohydric alcohols of different (0.4-1.6 nm) molecular lengths, and show that the finite lifetime of molecular species, and the proton-hole correlation length are the two principle parameters responsible for the dielectric response of all the studied alcohols across the entire frequency range. Our results demonstrate that a quantum non-rotational intermolecular mechanism drives the polarization in alcohols while the rotational mechanism of molecular polarization plays a secondary role, manifesting itself in the sub-terahertz region only.

physics.chem-ph↗

arXiv2019

Variational autoencoder reconstruction of complex many-body physics

Given the notably increasing complexity of mathematical models to study realistic systems and their coupling to their environment that constrains their dynamics, both analytical approaches and numerical methods that build on these models, show limitations in scope or applicability. On the other hand, machine learning, i.e. data-driven, methods prove to be increasingly efficient for the study of complex quantum systems. Deep neural networks in particular have been successfully applied to many-body quantum dynamics simulations and to quantum matter phase characterization. In the present work, we show how to use a variational autoencoder (VAE) -- a state-of-the-art tool in the field of deep learning for the simulation of probability distributions of complex systems. More precisely, we transform a quantum mechanical problem of many-body state reconstruction into a statistical problem, suitable for VAE, by using informationally complete positive operator-valued measure. We show with the paradigmatic quantum Ising model in a transverse magnetic field, that the ground-state physics, such as, e.g., magnetization and other mean values of observables, of a whole class of quantum many-body systems can be reconstructed by using VAE learning of tomographic data, for different parameters of the Hamiltonian, and even if the system undergoes a quantum phase transition. We also discuss challenges related to our approach as entropy calculations pose particular difficulties.

quant-ph↗

arXiv2018

Model predictive control of indoor microclimate: existing building stock comfort improvement

Home retrofitting provides a means to improve the basic energy and comfort characteristics of a building stock, which cannot be renewed because of prohibitive costs. We analyze how model predictive control (MPC) applied to indoor microclimate control can provide energy-efficient solutions to the problem of occupants' comfort in a variety of situations principally imposed by external weather and room occupancy. For this purpose we define an objective function for the energy consumption, and we consider two illustrative cases: one building designed and built in recent times with modern HVAC equipment, and one designed and built several decades ago with poor thermal characteristics and no dedicated ventilation system. Our model includes various physical effects such as air infiltration and indoor thermal "inertia mass" (inner walls, floor, ceiling, and furniture), and also accounts for the impact of human presence essentially as heat and CO$_2$ sources. The influence on the numerical results of forecast horizons and of uncertainties due to inaccuracies in the weather and room occupancy forecasts, are analyzed. As we solve non-convex optimization problems using a linear and a nonlinear optimizer, full MPC performance is compared to both linearized MPC and a standard on/off controller. The main advantage of MPC is its ability to provide satisfactory solutions for microclimate control at the least possible energy cost for both modern and old buildings. As old buildings are usually not properly ventilated, we see in light of our simulation results, that a supply ventilation system installation provides a solution for significant air quality improvement.

math.OC↗